The class-incremental object detection (iOD) setting considers classes to be observed incrementally over time and that the learner must adapt without retraining on old classes from scratch. The prevalent approaches [1]}, [2]}, [3]}, [4]} use knowledge distillation [5]} as a regularization measure to avoid forgetting old class information while training on new classes. Specifically, Shmelkov  [1]} repurpose Fast R-CNN for incremental learning by distilling classification and regression outputs from a previous stage model. Beside distilling model outputs, Chen  [4]} and Li  [2]} also distilled the intermediate network features. Hao  [3]} builds on Faster R-CNN and uses a student-teacher framework for RPN adaptation.
Acharya [10]} proposes a replay mechanism for online detection.
Recently, Peng  [11]} introduces an adaptive distillation technique into Faster R-CNN. Their methodology is the current state-of-the-art in iOD.
These methods cannot however work in an Open World environment, which is the focus of this work, and are unable to identify unknown objects.
